Requirements:

• Python 3 (probably at least 3.4)
• pipenv (pip3 install pipenv)
• tesseract (brew install tesseract, at least if you have a mac and homebrew working)
• imagemagick / ghostscript

The working/ subfolders contain a folder for each page. Each contains a page.png file that's the baseline page. It'll attempt to auto-deskew and crop each page.

pipenv install

make all at the top level should attempt to deskew, crop, split, and OCR everything, building CSV output in each working dir.

pipenv shell

make setup

make all

After that, concatenating all the page.csv files in each working dir should work.

csvstack working/*/page.csv > all_data.csv

Any skew of the type on the page greatly affects the reliability of page segmentation and OCR. If you want to manually override the automatic cropping/deskewing process, create a page-handcrop.png file in the page's working directory. Some already have them. Leaving a medium amount of white border on the page helps the OCR, as does removing any horizontal lines and noise. Be sure to leave the four thick vertical columnar separators, but the janky thin typeset lines that separate the odd/even columns can be removed. This sometimes helps with OCR if the addresses get too close to that line.

This includes a 1909.traineddata file which is based on the "best" english tesseract model, fine-tuned with more than 4000 hand-corrected examples from the scanned book. This, in theory, is slightly better at dealing with the type and typesetting of thee text.

make report should produce some reporting on how far your OCR process got, where it failed, and which pages have the best/worst OCR confidence. If certain processors simply refuse to run (the OCR will abort if there's a high error percentage), you can create a force-ocr file in that page directory and it'll force it through.